Social recommendation leverages the social connections between users to mitigate the issue of data sparsity and enhance recommendation quality. Although existing related works show their effectiveness, there remain two critical questions: i) The patterns of preference interactions among users are varied and heterogeneous. Current models struggle to accurately capture preference shifts from user interactions in noisy social environments. ii) Existing methods handle the integration of auxiliary information coarsely, potentially introducing noise and leading to biases in user preferences. To address the limitations above, we introduce a novel framework named Robust Graph Based Social Recommendation Through Contrastive Multi-View Learning (RGCML). This framework leverages denoised social relations and global intents as dual auxiliary information sources to provide comprehensive characterization of users. Firstly, RGCML employs the concept of opinion dynamics to simulate how user preferences evolve due to noisy social relations. Then, it utilizes a specifically designed information fusion module to extract critical contextual information from multiple semantic perspectives, thereby achieving personalized information fusion. Finally, it adopts the designed global-local contrastive learning paradigm that untangles and discriminates user preferences from global intents, further addressing the noise problem and enhancing the quality of user representations. Extensive experiments conducted on three real-world datasets demonstrate the superior performance of RGCML compared to several state-of-the-art (SOTA) baselines.